Epistemic virtues and ethics of explanation in machine learning

Abstract Many machine learning (ML) systems operate as blackboxes and offer limited insight into their internal mechanisms. This opacity creates tension between two crucial objectives optimizing technical performance and fulfilling ethical obligations for transparency. As this tension intensifies, especially in domains that often require justifiable decisions, this paper hopes to reconcile the engineering drive toward higher accuracy and reliability with epistemic virtues emphasizing honesty, accountability, and intellectual rigor. Drawing on philosophical theories of explanation and virtue epistemology, this paper also reviews various principles that embed ethical considerations into ML development and deployment processes. This study adopts a theoretical analysis to reveal how certain epistemic virtues (as exemplified by honesty, thoroughness, clarity, and open-mindedness) can guide ML design choices. In addition, we outline criteria for a balanced framework and policies that honor ethical demands. Finally, we stress the practical relevance of virtue-based approaches to knowledge production in modern technology for ethicists and epistemologists.